Many economic studies on inflation forecasting have found favorable results when inflation is modeled as a stationary process around a slowly time-varying trend. In contrast, the existing studies on interest rate forecasting either treat yields as being stationary, without any shifting endpoints, or treat yields as a random walk process. In this study we consider the problem of forecasting the term structure of interest rates with the assumption that the yield curve is driven by factors that are stationary around a time-varying trend. We compare alternative ways of modeling the time-varying trend. We find that allowing for shifting endpoints in yield curve factors can provide gains in the out-of-sample predictive accuracy, relative to stationary and random walk benchmarks. The results are both economically and statistically significant.